Temporal Knowledge Extraction From Large-scale Text Corpus

Knowledge, in practice, is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of harvesting temporal-aware knowledge, i.e., the relational facts coupled with their valid temporal interval. Inspired by pattern-based information extraction systems, we resort to temporal patterns to extract time-aware knowledge from free text. However, pattern design is extremely laborious and time consuming even for a single relation, and free text is usually ambiguous which makes temporal instance extraction extremely difficult. Therefore, in this work, we study the problem of temporal knowledge extraction with two steps: (1) temporal pattern extraction by automatically analysing a large-scale text corpus with a small number of seed temporal facts, (2) temporal instance extraction by applying the identified temporal patterns. For pattern extraction, we introduce various techniques, including corpus annotation, pattern generation, scoring and clustering, to improve both accuracy and coverage of the extracted patterns. For instance extraction, we propose a double-check strategy to improve the accuracy and a set of node-extension rules to improve the coverage. We conduct extensive experiments on real world datasets and compared with state-of-the-art systems. Experimental results verify the effectiveness of our proposed methods for temporal knowledge harvesting.